Locating healthcare services and designing its layout in a community is not an everyday decision for decision makers. The facility must be located and designed to meet both current and future demand and it must be optimized. Designing the facility layout is generally a Quadratic Assignment Problem (QAP). In this paper, it has been proposed to carry out a study of evolutionary models such as Ant Colony Optimization (ACO) for selecting optimal layout design for a healthcare unit. A survey is conducted in Goalpara district, Assam, India. In this paper, only ants or patients are considered who give only one visit to a particular department in the healthcare unit. That is why the number of interdepartmental movements of ant is taken as 1. The proposed layout is finally compared with the existing layout of the healthcare unit and checked for the effectiveness of the proposed layout. The techniques applied under the present investigation will definitely help the decision manager to select the best optimized facility layout for healthcare unit.

The facility layout problem is the arrangement of departments within a facility with respect to some objectives such as material handling cost and distance travelled. In an environment, where flows are fixed during the planning horizon, a static layout analysis would be sufficient. The solution procedure can be formulated as a quadratic assignment problem. In today’s market-based and dynamic environment, such flows can change quickly due to changes in the design of an existing product, the addition or deletion of a product, replacement of existing production equipment, shorter product life cycles and changes in the production quantities and associated production schedule. The flows or movements between pairs of departments in the layout are same but the distances may change. If this changes warrant it, layout rearrangements may be planned in one or more periods. The analysis is based on the tradeoff between the stable flow of inefficient layouts and added distance between the departments. However, layout analysis may not be justified in every situation. A Facility Layout Problem (FLP) is about arranging the physical departments or machines within a facility to help the facility work in a productive way. A poor layout can lead to accumulation of work-in process inventory, overloading of material handling system, inefficient setups and longer queues. Therefore, solution of an FLP is a strategic study to be conducted. Traditionally, there are two approaches for the FLP. The first one is the quantitative approach aiming at minimizing the total distance travelled between departments or machines based on a distance function. Due to the high complexity of computational of the FLP; there are many efficient methods which can find good solutions in an acceptable time. Rosenblatt (1986) was the first to present solution techniques for the Dynamic FLP (DFLP). He developed an optimal solution methodology, identified bounding procedures, and established heuristic techniques. Urban (1993) developed a steepest descent pair-wise exchange technique similar to CRAFT. Conway and Venkataramanan (1994) used a genetic algorithm to solve the DFLP, and Kaku and Mazzola (1997) used a tabu search heuristic. Balakrishnan et al. (2000) improved the pair-wise exchange heuristic by presenting a backward-pass pair-wise exchange heuristic with forecast windows. Baykasoglu and Gindy (2001) presented a Simulated Annealing (SA) heuristic, and Balakrishnan et al. (2003) presented a hybrid genetic algorithm for the DFLP. Dunker et al. (Thomas et al., 2005) combined evolutionary computation and dynamic programming for solving the DFLP.

Computer Sciences Journal, Quadratic Assignment Problem (QAP), Evolutionary Models, Ant Colony Optimization (ACO), Facility Layout Problem (FLP), Dynamic FLP (DFLP).